Abnormal Data Cleaning Method for Wind Turbines Based on Constrained Curve Fitting

• Published in: Energies (Basel) 2022-09, Vol.15 (17), p.6373
• Main Authors: Yin, Xiangqing, Liu, Yi, Yang, Li, Gao, Wenchao
• Format: Article
• Language: English
• Subjects: Air-turbines; Algorithms; Analysis; Cleaning; constrained curve fitting; Curve fitting; Data acquisition; data cleaning; Electricity; Electricity distribution; Energy; Fault diagnosis; Human error; Information management; Methods; Optimization; Penalty function; Process controls; Statistical methods; Supervisory control and data acquisition; Turbines; Wind power; Wind speed; wind turbine
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en15176373

With the increase of the scale of wind turbines, the problem of data quality of wind turbines has become increasingly prominent, which seriously affects the follow-up research. A large number of abnormal data exist in the historical data recorded by the wind turbine Supervisory Control And Data Acquisition (SCADA) system. In order to improve data quality, it is necessary to clean a large number of abnormal data in the original data. Aiming at the problem that the cleaning effect is not good in the presence of a large number of abnormal data, a method for cleaning abnormal data of wind turbines based on constrained curve fitting is proposed. According to the wind speed-power characteristics of wind turbines, the constrained wind speed-power curve is fit with the least square method, and the constrained optimization problem is transformed into an unconstrained optimization problem by using the external penalty function method. Data cleaning was performed on the fitted curve using an improved 3-σ standard deviation. Experiments show that, compared with the existing methods, this method can still perform data cleaning well when the historical wind turbine data contains many abnormal data, and the method is insensitive to parameters, simple in the calculation, and easy to automate.